/*============================================================================

The Medical Imaging Interaction Toolkit (MITK)

Copyright (c) German Cancer Research Center (DKFZ)
All rights reserved.

Use of this source code is governed by a 3-clause BSD license that can be
found in the LICENSE file.

============================================================================*/

/*=========================================================================
*
*  Copyright Insight Software Consortium
*
*  Licensed under the Apache License, Version 2.0 (the "License");
*  you may not use this file except in compliance with the License.
*  You may obtain a copy of the License at
*
*         http://www.apache.org/licenses/LICENSE-2.0.txt
*
*  Unless required by applicable law or agreed to in writing, software
*  distributed under the License is distributed on an "AS IS" BASIS,
*  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
*  See the License for the specific language governing permissions and
*  limitations under the License.
*
*=========================================================================*/
#ifndef __itkEnhancedScalarImageToTextureFeaturesFilter_h
#define __itkEnhancedScalarImageToTextureFeaturesFilter_h

#include "itkDataObjectDecorator.h"

#include "itkEnhancedHistogramToTextureFeaturesFilter.h"
#include "itkScalarImageToCooccurrenceMatrixFilter.h"

namespace itk
{
  namespace Statistics
  {
    /** \class EnhancedScalarImageToTextureFeaturesFilter
    *  \brief This class computes texture descriptions from an image.
    *
    * This class computes features that summarize the texture of a given image.
    * The texture features are computed a la Haralick, and have proven to be useful
    * in image classification for biological and medical imaging.
    * This class computes the texture features of an image (optionally in a
    * masked region), averaged across several spatial directions so that they are
    * invariant to rotation.
    *
    * By default, texure features are computed for each spatial
    * direction and then averaged afterward, so it is possible to access the standard
    * deviations of the texture features. These values give a clue as to texture
    * anisotropy. However, doing this is much more work, because it involved computing
    * one GLCM for each offset given. To compute a single GLCM using the first offset ,
    * call FastCalculationsOn(). If this is called, then the texture standard deviations
    * will not be computed (and will be set to zero), but texture computation will
    * be much faster.
    *
    * This class is templated over the input image type.
    *
    * Template Parameters:
    * The image type, and the type of histogram frequency container. If you are using
    * a large number of bins per axis, a sparse frequency container may be advisable.
    * The default is to use a dense frequency container.
    *
    * Inputs and parameters:
    * -# An image
    * -# A mask defining the region over which texture features will be
    *    calculated. (Optional)
    * -# The pixel value that defines the "inside" of the mask. (Optional, defaults
    *    to 1 if a mask is set.)
    * -# The set of features to be calculated. These features are defined
    *    in the GreyLevelCooccurrenceMatrixTextureCoefficientsCalculator class. (Optional,
    *    defaults to {Energy, Entropy, InverseDifferenceMoment, Inertia, ClusterShade,
    *    ClusterProminence}, as in Conners, Trivedi and Harlow.)
    * -# The number of intensity bins. (Optional, defaults to 256.)
    * -# The set of directions (offsets) to average across. (Optional, defaults to
    *    {(-1, 0), (-1, -1), (0, -1), (1, -1)} for 2D images and scales analogously for ND
    *    images.)
    * -# The pixel intensity range over which the features will be calculated.
    *    (Optional, defaults to the full dynamic range of the pixel type.)
    *
    * In general, the default parameter values should be sufficient.
    *
    * Outputs:
    * (1) The average value of each feature.
    * (2) The standard deviation in the values of each feature.
    *
    * Web reference:
    * http://www.fp.ucalgary.ca/mhallbey/tutorial.htm
    *
    * Print references:
    * Haralick, R.M., K. Shanmugam and I. Dinstein. 1973.  Textural Features for
    * Image Classification. IEEE Transactions on Systems, Man and Cybernetics.
    * SMC-3(6):610-620.
    *
    * Haralick, R.M. 1979. Statistical and Structural Approaches to Texture.
    * Proceedings of the IEEE, 67:786-804.
    *
    * R.W. Conners and C.A. Harlow. A Theoretical Comaprison of Texture Algorithms.
    * IEEE Transactions on Pattern Analysis and Machine Intelligence,  2:204-222, 1980.
    *
    * R.W. Conners, M.M. Trivedi, and C.A. Harlow. Segmentation of a High-Resolution
    * Urban Scene using Texture  Operators. Computer Vision, Graphics and Image
    * Processing, 25:273-310,  1984.
    *
    * \sa ScalarImageToCooccurrenceMatrixFilter
    * \sa HistogramToTextureFeaturesFilter
    *
    * Author: Zachary Pincus
    * \ingroup ITKStatistics
    */

    template< typename TImageType,
      typename THistogramFrequencyContainer = DenseFrequencyContainer2 >
    class EnhancedScalarImageToTextureFeaturesFilter:public ProcessObject
    {
    public:
      /** Standard typedefs */
      typedef EnhancedScalarImageToTextureFeaturesFilter Self;
      typedef ProcessObject                      Superclass;
      typedef SmartPointer< Self >               Pointer;
      typedef SmartPointer< const Self >         ConstPointer;

      /** Run-time type information (and related methods). */
      itkTypeMacro(EnhancedScalarImageToTextureFeaturesFilter, ProcessObject);

      /** standard New() method support */
      itkNewMacro(Self);

      typedef THistogramFrequencyContainer FrequencyContainerType;
      typedef TImageType                   ImageType;
      typedef typename ImageType::Pointer  ImagePointer;

      typedef typename ImageType::PixelType                PixelType;
      typedef typename ImageType::OffsetType               OffsetType;
      typedef VectorContainer< unsigned char, OffsetType > OffsetVector;
      typedef typename OffsetVector::Pointer               OffsetVectorPointer;
      typedef typename OffsetVector::ConstPointer          OffsetVectorConstPointer;

      typedef ScalarImageToCooccurrenceMatrixFilter<
        ImageType, FrequencyContainerType >               CooccurrenceMatrixFilterType;

      typedef typename CooccurrenceMatrixFilterType::HistogramType HistogramType;
      typedef EnhancedHistogramToTextureFeaturesFilter< HistogramType >    TextureFeaturesFilterType;

      typedef short                                                  TextureFeatureName;
      typedef VectorContainer< unsigned char, TextureFeatureName >   FeatureNameVector;

      typedef typename FeatureNameVector::Pointer      FeatureNameVectorPointer;
      typedef typename FeatureNameVector::ConstPointer FeatureNameVectorConstPointer;
      typedef VectorContainer< unsigned char, double > FeatureValueVector;
      typedef typename FeatureValueVector::Pointer     FeatureValueVectorPointer;

      /** Smart Pointer type to a DataObject. */
      typedef DataObject::Pointer DataObjectPointer;

      /** Type of DataObjects used for scalar outputs */
      typedef DataObjectDecorator< FeatureValueVector >
        FeatureValueVectorDataObjectType;

      const FeatureValueVectorDataObjectType * GetFeatureMeansOutput() const;

      const FeatureValueVectorDataObjectType * GetFeatureStandardDeviationsOutput() const;

      /** Connects the input image for which the features are going to be computed
      */
      using Superclass::SetInput;
      void SetInput(const ImageType *);

      const ImageType * GetInput() const;

      /** Return the feature means and deviations.  */
      itkGetConstReferenceObjectMacro(FeatureMeans, FeatureValueVector);
      itkGetConstReferenceObjectMacro(FeatureStandardDeviations, FeatureValueVector);

      /** Set the desired feature set. Optional, for default value see above. */
      itkSetConstObjectMacro(RequestedFeatures, FeatureNameVector);
      itkGetConstObjectMacro(RequestedFeatures, FeatureNameVector);

      /** Set the  offsets over which the co-occurrence pairs will be computed.
      Optional; for default value see above. */
      itkSetConstObjectMacro(Offsets, OffsetVector);
      itkGetConstObjectMacro(Offsets, OffsetVector);

      /** Set number of histogram bins along each axis.
      Optional; for default value see above. */
      void SetNumberOfBinsPerAxis(unsigned int);

      /** Set the min and max (inclusive) pixel value that will be used for
      feature calculations. Optional; for default value see above. */
      void SetPixelValueMinMax(PixelType min, PixelType max);

      /** Connects the mask image for which the histogram is going to be computed.
      Optional; for default value see above. */
      void SetMaskImage(const ImageType *);

      const ImageType * GetMaskImage() const;

      /** Set the pixel value of the mask that should be considered "inside" the
      object. Optional; for default value see above. */
      void SetInsidePixelValue(PixelType InsidePixelValue);

      itkGetConstMacro(FastCalculations, bool);
      itkSetMacro(FastCalculations, bool);
      itkBooleanMacro(FastCalculations);

    protected:
      EnhancedScalarImageToTextureFeaturesFilter();
      ~EnhancedScalarImageToTextureFeaturesFilter() override {}
      void PrintSelf(std::ostream & os, Indent indent) const ITK_OVERRIDE;

      void FastCompute();

      void FullCompute();

      /** This method causes the filter to generate its output. */
      void GenerateData() ITK_OVERRIDE;

      /** Make a DataObject to be used for output output. */
      typedef ProcessObject::DataObjectPointerArraySizeType DataObjectPointerArraySizeType;
      using Superclass::MakeOutput;
      DataObjectPointer MakeOutput(DataObjectPointerArraySizeType) ITK_OVERRIDE;

    private:
      typename CooccurrenceMatrixFilterType::Pointer m_GLCMGenerator;

      typename TextureFeaturesFilterType::Pointer    m_GLCMCalculator;

      FeatureValueVectorPointer     m_FeatureMeans;
      FeatureValueVectorPointer     m_FeatureStandardDeviations;
      FeatureNameVectorConstPointer m_RequestedFeatures;
      OffsetVectorConstPointer      m_Offsets;
      bool                          m_FastCalculations;
    };
  } // end of namespace Statistics
} // end of namespace itk

#ifndef ITK_MANUAL_INSTANTIATION
#include "itkEnhancedScalarImageToTextureFeaturesFilter.hxx"
#endif

#endif
